Majed Alhaisoni

TL;DR: An automated multimodal classification method using deep learning for brain tumor type classification using two pre-trained convolutional neural network models for feature extraction and a correntropy-based joint learning approach for the selection of best features.

TL;DR: This work presents a sustainable deep learning architecture, which utilizes multi-layer deep features fusion and selection, for accurate object classification, and shows significantly more accuracy than existing methods.

TL;DR: A fully automated system for stomach infection recognition based on deep learning features fusion and selection and achieved an accuracy of 98.4%, which is best as compared to existing state-of-the-art techniques.

TL;DR: A new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features is proposed, which outperforms recent techniques.

TL;DR: A novel framework for skin lesion classification is proposed, which integrates deep features information to generate most discriminant feature vector, with an advantage of preserving the original feature space, and is validated on four benchmark dermoscopic datasets.